Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor

被引：0

作者：

Hussain, Muhammad ^{[1
]}

Al-Otaibi, Sarah ^{[1
]}

Muhammad, Ghulam ^{[1
]}

Aboalsamh, Hatim ^{[1
]}

Bebis, George ^{[2
]}

Mirza, Anwar M. ^{[1
]}

机构：

[1] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

[2] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA

来源：

IMAGE ANALYSIS, SCIA 2013: 18TH SCANDINAVIAN CONFERENCE | 2013年 / 7944卷

关键词：

Gender recognition; Face recognition; WLD Descriptor; Nonsubsampled Contourlet Transform; Support Vector Machines; FUSION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gender recognition using facial images plays an important role in biometric technology. Multiscale texture descriptors perform better in gender recognition because they encode the multiscale facial microstructures in a better way. We present a gender recognition system that uses SVM, two-stage feature selection and multiscale texture feature based on Nonsubsampled Contourlet Transform and Weber law descriptor (NSCT-WLD). The proposed system has better recognition rate (99.50%) than the state-of-the-art methods on FERET database. This research also reveals that in NSCT decomposition what is essential for face recognition and what is important for other tasks like age detection.

引用

页码：373 / 383

页数：11

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